Unsupervised learning of anomalous diffusion data
This work addresses the challenge of applying theoretical diffusion models to experimental data in physics, offering an unsupervised alternative where supervised learning is not feasible.
The authors tackled the problem of characterizing anomalous diffusion from short, noisy experimental trajectories by developing an unsupervised learning method that learns diffusion characteristics without labeled data, enabling discrimination between models, extraction of physical parameters, and identification of novel diffusion types, with validation on experimental data.
The characterization of diffusion processes is a keystone in our understanding of a variety of physical phenomena. Many of these deviate from Brownian motion, giving rise to anomalous diffusion. Various theoretical models exists nowadays to describe such processes, but their application to experimental setups is often challenging, due to the stochastic nature of the phenomena and the difficulty to harness reliable data. The latter often consists on short and noisy trajectories, which are hard to characterize with usual statistical approaches. In recent years, we have witnessed an impressive effort to bridge theory and experiments by means of supervised machine learning techniques, with astonishing results. In this work, we explore the use of unsupervised methods in anomalous diffusion data. We show that the main diffusion characteristics can be learnt without the need of any labelling of the data. We use such method to discriminate between anomalous diffusion models and extract their physical parameters. Moreover, we explore the feasibility of finding novel types of diffusion, in this case represented by compositions of existing diffusion models. At last, we showcase the use of the method in experimental data and demonstrate its advantages for cases where supervised learning is not applicable.